%0 Generic %A Long, Xiang %A Beddow, Luke %A Hadjivelichkov, Denis %A Delfaki, Andromachi Maria %A Wurdemann, Helge %A Kanoulas, Dimitrios %C Ha Long, Vietnam %D 2024 %F discovery:10178561 %I IEEE %K Location awareness, Affordances, Pipelines, Grasping, System integration, Robots, Videos %T Reinforcement Learning-based Grasping via One-Shot Affordance Localization and Zero-Shot Contrastive Language–Image Learning %U https://discovery.ucl.ac.uk/id/eprint/10178561/ %X We present a novel robotic grasping system using a caging-style gripper, that combines one-shot affordance localization and zero-shot object identification. We demonstrate an integrated system requiring minimal prior knowledge, focusing on flexible few-shot object agnostic approaches. For grasping a novel target object, we use as input the color and depth of the scene, an image of an object affordance similar to the target object, and an up to three-word text prompt describing the target object. We demonstrate the system using real-world grasping of objects from the YCB benchmark set, with four distractor objects cluttering the scene. Overall, our pipeline has a success rate of the affordance localization of 96%, object identification of 62.5%, and grasping of 72%. Videos are on the project website: https://sites.google.com/view/ rl-affcorrs-grasp. %Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.